topic stringclasses 2
values | relevance score int64 1 10 | paper name stringlengths 19 239 | text stringlengths 1.56k 680k |
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synthetic_cpt | 2 | Targeted_Angular_Reversal_of_Weights_(TARS)_for_Knowledge_Removal_in_Large_Language_Models.pdf | 4
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Targeted Angular Reversal of Weights (TARS) for
Knowledge Removal in Large Language Models
Harry J. Davies
Department of Electrical Engineering
Imperial College London
London, UK
harry.davies14@imperial.ac.uk
Giorgos Iacovides
Department of Electr... |
synthetic_cpt | 2 | IdeaBench_Benchmarking_Large_Language_Models_for_Research_Idea_Generation.pdf | IdeaBench: Benchmarking Large Language Models for Research Idea Generation
Sikun Guo*, Amir Hassan Shariatmadari*, Guangzhi Xiong, Albert Huang, Eric Xie, Stefan
Bekiranov, Aidong Zhang
University of Virginia
{qkm6sq, ahs5ce, hhu4zu, kfa7fg, jrg4wx, sb3de, aidong}@virginia.edu
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synthetic_cpt | 1 | GoldMiner_Elastic_Scaling_of_Training_Data_Pre-Processing_Pipelines_for_Deep_Learning.pdf | GOLD Mine: A new Galaxy Database on the WEB
Gavazzi G., Boselli A., Donati A., Franzetti P., Scodeggio M.
The galaxy database ”GOLDmine” (http://goldmine.mib.infn.it/) has been significantly up-
dated (Sept/1/2003) (see ”Introducing GOLD Mine: A new Galaxy Database on the WEB”
by Gavazzi et al. 2003, Astronomy & Astro... |
synthetic_cpt | 1 | A_Closer_Look_at_Data_Augmentation_Strategies_for_Finetuning-Based_LowFew-Shot_Object_Detection.pdf | A Closer Look at Data Augmentation Strategies for
Finetuning-Based Low/Few-Shot Object Detection
Vladislav Li∗, Georgios Tsoumplekas†, Ilias Siniosoglou†‡, Vasileios Argyriou∗, Anastasios Lytos§,
Eleftherios Fountoukidis§ and Panagiotis Sarigiannidis†‡
Abstract—Current methods for low- and few-shot object de-
tectio... |
synthetic_cpt | 3 | IterAlign_Iterative_Constitutional_Alignment_of_Large_Language_Models.pdf | 4
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ITERALIGN: Iterative Constitutional Alignment of Large Language Models
Xiusi Chen1 Hongzhi Wen2 Sreyashi Nag3 Chen Luo3
Qingyu Yin3 Ruirui Li3 Zheng Li3 Wei Wang1
University of California, Los Angeles1 Michigan State University2 Amazon3
{xchen,w... |
synthetic_cpt | 4 | CraftRTL_High-quality_Synthetic_Data_Generation_for_Verilog_Code_Models_with_Correct-by-Construction_Non-Textual_Representations_and_Targeted_Code_Repair.pdf | 4
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Preprint
CRAFTRTL: HIGH-QUALITY
SYNTHETIC DATA
GENERATION FOR VERILOG CODE MODELS WITH
CORRECT-BY-CONSTRUCTION NON-TEXTUAL REP-
RESENTATIONS AND TARGETED CODE REPAIR
Mingjie Liu∗, Yun-Da Tsai∗, Wenfei Zhou, Haoxing Ren
NVIDIA Corporation
{mingji... |
synthetic_cpt | 1 | Evaluation_Metrics_of_Language_Generation_Models_for_Synthetic_Traffic_Generation_Tasks.pdf | Evaluation Metrics of Language Generation Models
for Synthetic Traffic Generation Tasks
Simone Filice2, Jason Ingyu Choi1, Giuseppe Castellucci1, Eugene Agichtein1, Oleg Rokhlenko1
1Amazon, Seattle - USA
2Amazon, Tel Aviv - Israel
{filicesf,chojson,giusecas,eugeneag,olegro}@amazon.com
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synthetic_cpt | 1 | Automated_Optical_Inspection_for_Printed_Circuit_Board_Assembly_Manufacturing_with_Transfer_Learning_and_Synthetic_Data_Generation.pdf | DVQI: A Multi-task, Hardware-integrated Artificial Intelligence System for
Automated Visual Inspection in Electronics Manufacturing
Audrey G. Chung1, Francis Li1, Jeremy Ward1, Andrew Hryniowski1,2,3, Alexander Wong1,2,3
1DarwinAI Corp., Waterloo, ON
2Vision and Image Processing Research Group, University of Waterloo
... |
synthetic_cpt | 3 | Beyond_Classification_Financial_Reasoning_in_State-of-the-Art_Language_Models.pdf | 0
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Spectral Classification; Old and Contemporary
Sunetra Giridhar
Indian Institute of Astrophysics, Bangalore 560034, India
Summary. Beginning with a historical account of the spectral classification, its re-
finement through additional cr... |
synthetic_cpt | 3 | Synthetic_Data_Augmentation_Using_Large_Language_Models_(LLM)_A_Case-Study_of_the_Kamyr_Digester.pdf | 4
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Synthetic Test Collections for Retrieval Evaluation
Hossein A. Rahmani
University College London
London, UK
hossein.rahmani.22@ucl.ac.uk
Nick Craswell
Microsoft
Bellevue, US
nickcr@microsoft.com
Emine Yilmaz
University College London & Amazon
... |
synthetic_cpt | 2 | Mitigating_Bias_in_Large_Language_Models_A_Multi-Task_Training_Approach_Using_BERT.pdf | 3
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Multilingual Bias Detection and Mitigation for
Indian Languages
Ankita Maity1, Anubhav Sharma1, Rudra Dhar1, Tushar Abhishek1,2,
Manish Gupta1,2, and Vasudeva Varma1
1 IIIT Hyderabad, India
2 Microsoft, India
Abstract. Lack of diverse perspective... |
synthetic_cpt | 5 | Knowledge_Distillation_Using_Frontier_Open-source_LLMs_Generalizability_and_the_Role_of_Synthetic_Data.pdf | KNOWLEDGE DISTILLATION USING FRONTIER OPEN-SOURCE
LLMS: GENERALIZABILITY AND THE ROLE OF SYNTHETIC
DATA
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Microsoft
Anup Shirgaonkar∗†, Nikhil Pandey†, Nazmiye Ceren Abay, Tolga Aktas, Vijay Aski
ABSTRACT
Leading open-source large language models... |
synthetic_cpt | 2 | Can_Language_Models_Enable_In-Context_Database.pdf | PHONEME LEVEL LANGUAGE MODELS FOR SEQUENCE BASED LOW RESOURCE ASR
Siddharth Dalmia, Xinjian Li, Alan W Black and Florian Metze
Language Technologies Institute, Carnegie Mellon University; Pittsburgh, PA; U.S.A.
{sdalmia|xinjianl|awb|fmetze}@cs.cmu.edu
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synthetic_cpt | 1 | Solving_Quantitative_Reasoning_Problems_with_Language_Models.pdf | 2
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Solving Quantitative Reasoning Problems with
Language Models
Aitor Lewkowycz∗, Anders Andreassen†, David Dohan†, Ethan Dyer†, Henryk Michalewski†,
Vinay Ramasesh†, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo,
Yuhuai Wu, Behnam Neyshabu... |
synthetic_cpt | 1 | Log-likelihood_Ratio_for_Low-Density_Parity-Check_Codes_Under_Binary_Symmetric_Erasure_Channel_in_DNA_Storage.pdf | 1
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A Dynamic Stabbing-Max Data Structure with Sub-Logarithmic
Query Time
Yakov Nekrich∗
Abstract
In this paper we describe a dynamic data structure that answers one-dimensional stabbing-
max queries in optimal O(log n/ log log n) time. Our data struc... |
synthetic_cpt | 1 | PaLM_Scaling_Language_Modeling_with_Pathways.pdf | A Novel Remote Sensing Approach to Recognize and Monitor Red Palm Weevil
in Date Palm Trees (manuscript)
Yashu Kang1, Chunlei Chen1, Fujian Cheng1, Jianyong Zhang1
1 STAR VISION
March 20, 2022
Abstract
The spread of the Red Pal Weevil (RPW) has become an existential threat for palm trees around
the world. I... |
synthetic_cpt | 1 | Deep_Learning-based_Assessment_of_Oncologic_Outcomes_from_Natural_Language_Processing_of_Structured_Radiology_Reports.pdf | 8
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Opening the black box of deep learning
Dian Lei , Xiaoxiao Chen , Jianfei Zhao
School of Mechatronics Engineering and Automation,
Shanghai University, Shanghai 200072, China
Abstract
The great success of deep learning shows that its technology ... |
synthetic_cpt | 2 | ZMM-TTS_Zero-Shot_Multilingual_and_Multispeaker_Speech_Synthesis_Conditioned_on_Self-Supervised_Discrete_Speech_Representations.pdf | 7
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Performance Evaluation of the Zero-Multipole
Summation Method in Modern Molecular Dynamics
Software
Shun Sakurabaa,∗, Ikuo Fukudab
aGraduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha,
Kashiwa-shi,... |
synthetic_cpt | 2 | CLIPSonic_Text-to-Audio_Synthesis_with_Unlabeled_Videos_and_Pretrained_Language-Vision_Models.pdf | CLIPSONIC: TEXT-TO-AUDIO SYNTHESIS WITH UNLABELED VIDEOS
AND PRETRAINED LANGUAGE-VISION MODELS
Hao-Wen Dong1,2∗ Xiaoyu Liu1
Jordi Pons1 Gautam Bhattacharya1
Santiago Pascual1
Joan Serr`a1 Taylor Berg-Kirkpatrick2
Julian McAuley2
1 Dolby Laboratories
2 University of California San Diego
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synthetic_cpt | 3 | Self-Evolved_Diverse_Data_Sampling_for_Efficient_Instruction_Tuning.pdf | 1
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Non-abelian self-duality from self-interaction
A. Khoudeir
Instituto de F´ısica, Universidad Nacional Aut´onoma de M´exico
Apdo. Postal 20-364, 01000 M´exico D. F. M´exico
and
Centro de Astrof´ısica Te´orica, Departamento de F´ısica, Facultad de
Ciencia... |
synthetic_cpt | 8 | Evaluating_Language_Models_as_Synthetic_Data_Generators.pdf | 3
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Generating Faithful Synthetic Data with Large Language Models:
A Case Study in Computational Social Science
Veniamin Veselovsky†, Manoel Horta Ribeiro†, Akhil Arora†,
Martin Josifoski†, Ashton Anderson∗, Robert West†
† EPFL ∗University of Toronto
... |
synthetic_cpt | 6 | First_Train_to_Generate_then_Generate_to_Train_UnitedSynT5_for_Few-Shot_NLI.pdf | GenCo: Generative Co-training for Generative Adversarial Networks with
Limited Data
Kaiwen Cui*, Jiaxing Huang*, Zhipeng Luo, Gongjie Zhang, Fangneng Zhan, Shijian Lu†
School of Computer Science Engineering, Nanyang Technological University
{kaiwen001, zhipeng001}@e.ntu.edu.sg, {jiaxing.huang, Gongjiezhang, fnzhan, sh... |
synthetic_cpt | 2 | Logic-LM_Empowering_Large_Language_Models_with_Symbolic_Solvers_for_Faithful_Logical_Reasoning.pdf | Abstraction Logic
A New Foundation for (Computer) Mathematics
Steven Obua
obua@practal.com
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Abstract. Abstraction logic is a new logic, serving as a foundation of mathematics. It
combines features of both predicate logic and higher-order logic:... |
synthetic_cpt | 4 | PULSAR_at_MEDIQA-Sum_2023_Large_Language_Models_Augmented_by_Synthetic_Dialogue_Convert_Patient_Dialogues_to_Medical_Records.pdf | PULSAR at MEDIQA-Sum 2023: Large Language
Models Augmented by Synthetic Dialogue Convert
Patient Dialogues to Medical Records
Viktor Schlegel1,2, Hao Li2, Yuping Wu2, Anand Subramanian1,3,
Thanh-Tung Nguyen1, Abhinav Ramesh Kashyap1, Daniel Beck4, Xiaojun Zeng2,
Riza Theresa Batista-Navarro2, Stefan Winkler1,3 and Gor... |
synthetic_cpt | 2 | The_Trade-offs_of_Domain_Adaptation_for_Neural_Language_Models.pdf | epl draft
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Quantifying immediate price impact of trades based on the k-shell
decomposition of stock trading networks
Wen-Jie Xie1,2,3, Ming-Xia Li2,3,4, Hai-Chuan Xu1,2,3, Wei Chen5, Wei-Xing Zhou1,3,6 (a) and H. Eugene
Stanley7
1 Department ... |
synthetic_cpt | 3 | Concept-skill_Transferability-based_Data_Selection_for_Large_Vision-Language_Models.pdf | Concept Generation in Language Evolution
Martha Lewis, Jonathan Lawry
Department of Engineering Mathematics, University of Bristol, BS8 1TR, UK
martha.lewis@bristol.ac.uk, j.lawry@bristol.ac.uk
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This thesis investigates the generation ... |
synthetic_cpt | 3 | Filling_in_the_Gaps_LLM-Based_Structured_Data_Generation_from_Semi-Structured_Scientific_Data.pdf | Filling Gaps in Chaoti
Time Series
Fran
es
o Paparella
Dipartimento di Matemati
a (cid:16)E. de Giorgi(cid:17)
Università di Le
e
Le
e, Italy
∗
Abstra
t
We propose a method for (cid:28)lling arbitrarily wide gaps in deterministi
time series. Cru
ial to the
method is the ability to apply Takens' theorem in o... |
synthetic_cpt | 2 | Increasing_The_Performance_of_Cognitively_Inspired_Data-Efficient_Language_Models_via_Implicit_Structure_Building.pdf | Can humans help BERT gain
“confidence”?
Piyush Agrawal | 250944
Submitted for the degree of MSc Artificial Intelligence and Adaptive Systems
University of Sussex
30th Aug, 2022
1
Declaration
I hereby declare that this project has not been and will not be submitted in whole or in part to
another ... |
synthetic_cpt | 1 | Smoothie_Label_Free_Language_Model_Routing.pdf | SMOOTHIE: Label Free Language Model Routing
Neel Guha∗1
Mayee F. Chen *1
Trevor Chow1
Ishan S. Khare1
Christopher Ré1
1Department of Computer Science, Stanford University
December 9, 2024
Abstract
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Large language models (LLMs) are increasingl... |
synthetic_cpt | 1 | Effectiveness_of_Data_Augmentation_and_Pretraining_for_Improving_Neural_Headline_Generation_in_Low-Resource_Settings.pdf | Low-Resource Neural Headline Generation
Ottokar Tilk and Tanel Alum¨ae
Department of Software Science, School of Information Technologies,
Tallinn University of Technology, Estonia
ottokar.tilk@ttu.ee, tanel.alumae@ttu.ee
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Recent neural... |
synthetic_cpt | 6 | Aioli_A_Unified_Optimization_Framework_for_Language_Model_Data_Mixing.pdf | 4
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Aioli: A unified optimization framework for language model data
mixing
Mayee F. Chen*1
Michael Y. Hu⋆2
Nicholas Lourie3
Kyunghyun Cho2,3,4
Christopher Ré1
1Department of Computer Science, Stanford University
2Center for Data Science, New York ... |
synthetic_cpt | 1 | Appeal_for_Attention_at_SemEval-2023_Task_3_Data_augmentation_extension_strategies_for_detection_of_online_news_persuasion_techniques.pdf | Neobility at SemEval-2017 Task 1: An Attention-based Sentence
Similarity Model
WenLi Zhuang ∗
Shan-Si Elementary School
ChangHua County, Taiwan
bibo9901@gmail.com
Ernie Chang
Department of Linguistics
University of Washington
Seattle, WA 98195, USA
cyc025@uw.edu
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synthetic_cpt | 3 | SimRAG_Self-Improving_Retrieval-Augmented_Generation_for_Adapting_Large_Language_Models_to_Specialized_Domains.pdf | SimRAG: Self-Improving Retrieval-Augmented Generation for
Adapting Large Language Models to Specialized Domains
Ran Xu1,2*, Hui Liu2, Sreyashi Nag2, Zhenwei Dai2, Yaochen Xie2, Xianfeng Tang2,
Chen Luo2, Yang Li2, Joyce C. Ho1, Carl Yang1, Qi He2
1 Emory University 2 Amazon
{ran.xu,joyce.c.ho,j.carlyang}@emory.edu, li... |
synthetic_cpt | 4 | Generating_Synthetic_Resume_Data_with_Large_Language_Models_for_Enhanced_Job_Description_Classification.pdf | Distilling Large Language Models using Skill-Occupation Graph Context
for HR-Related Tasks
Pouya Pezeshkpour1 Hayate Iso1 Thom Lake2
Nikita Bhutani1 Estevam Hruschka1
2Indeed
{pouya,hayate,nikita,estevam}@megagon.ai
1Megagon Labs
tlake@indeed.com
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synthetic_cpt | 1 | Automated_Depth_Dataset_Generation_with_Integrated_Quality_Metrics_for_Robotic_Manipulation.pdf | IEEE ROBOTICS AND AUTOMATION LETTERS. PREPRINT VERSION. ACCEPTED JUNE, 2022
1
TransCG: A Large-Scale Real-World Dataset for
Transparent Object Depth Completion and A
Grasping Baseline
Hongjie Fang1, Hao-Shu Fang1, Sheng Xu1 and Cewu Lu2
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Abstra... |
synthetic_cpt | 1 | GLaM_Efficient_Scaling_of_Language_Models_with_Mixture-of-Experts.pdf | 2
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Full Contextual Attention for Multi-resolution Transformers in Semantic
Segmentation
Loic Themyr1,2
Clement Rambour1
Nicolas Thome1,3
Toby Collins2
Alexandre Hostettler2
1Conservatoire National des Arts et M´etiers, Paris, France
2IRCAD, Stras... |
synthetic_cpt | 5 | Principle-Driven_Self-Alignment_of_Language_Models_from_Scratch_with_Minimal_Human_Supervision.pdf | 4
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AN INDUCTION PRINCIPLE AND
PIGEONHOLE PRINCIPLES FOR K-FINITE SETS
Andreas Blass
Abstract. We establish a course-of-values induction principle for K-finite sets in
intuitionistic type theory. Using this principle, we prove a pigeonhole ... |
synthetic_cpt | 2 | Training_language_models_to_follow_instructions_with_human_feedback.pdf | The Wisdom of Hindsight Makes Language Models
Better Instruction Followers
Tianjun Zhang * 1 Fangchen Liu * 1 Justin Wong 1 Pieter Abbeel 1 Joseph E. Gonzalez 1
Abstract
Reinforcement learning has seen wide success in
finetuning large language models to better align
with instructions via human feedback. The so-
calle... |
synthetic_cpt | 5 | West-of-N_Synthetic_Preferences_for_Self-Improving_Reward_Models.pdf | The non-linear dual gravity equation of motion in eleven dimensions
Keith Glennon and Peter West
Department of Mathematics
King’s College, London WC2R 2LS, UK
Abstract
We derive the non-linear dual graviton equation of motion in eleven dimensions in the
context of E theory.
E11 knows best.
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synthetic_cpt | 7 | Real-Fake_Effective_Training_Data_Synthesis_Through_Distribution_Matching.pdf | 5
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Real open books and real contact structures
Ferit ¨OZT ¨URK and Nermin SALEPC˙I
Abstract. A real 3-manifold is a smooth 3-manifold together with
an orientation preserving smooth involution, called a real struc-
ture. In this article we study ope... |
synthetic_cpt | 1 | FedSyn_Synthetic_Data_Generation_using_Federated_Learning.pdf | FedSyn: Synthetic Data Generation using Federated
Learning
Monik Raj Behera1, Sudhir Upadhyay1, Suresh Shetty1, Sudha Priyadarshini1, Palka Patel1, Ker Farn Lee1
{monik.r.behera,sudhir.x.upadhyay,suresh.shetty,sudha.priyadarshini}
@jpmorgan.com
1Onyx by J.P. Morgan
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synthetic_cpt | 2 | Stay_on_topic_with_Classifier-Free_Guidance.pdf | COVID-19: Detecting Depression Signals during Stay-At-Home Period
Jean Marie Tshimula,1 Belkacem Chikhaoui,1,2 Shengrui Wang1
1Department of Computer Science, Universit´e de Sherbrooke, QC J1K 2R1, Canada
2LICEF Research Center, Universit´e T ´ELUQ, QC H2S 3L5, Canada
{kabj2801,shengrui.wang}@usherbrooke.ca
belkacem.c... |
synthetic_cpt | 3 | Self-Explained_Keywords_Empower_Large_Language_Models_for_Code_Generation.pdf | 1
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Non-abelian self-duality from self-interaction
A. Khoudeir
Instituto de F´ısica, Universidad Nacional Aut´onoma de M´exico
Apdo. Postal 20-364, 01000 M´exico D. F. M´exico
and
Centro de Astrof´ısica Te´orica, Departamento de F´ısica, Facultad de
Ciencia... |
synthetic_cpt | 1 | Synthesizing_Neural_Network_Controllers_with_Probabilistic_Model-Based_Reinforcement_Learning.pdf | Synthesizing Neural Network Controllers with Probabilistic
Model-Based Reinforcement Learning
Juan Camilo Gamboa Higuera, David Meger, and Gregory Dudek
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Abstract— We present an algorithm for rapidly learning
neural network policies for robotics sy... |
synthetic_cpt | 2 | Scaling_Law_for_Post-training_after_Model_Pruning.pdf | P2 Law: Scaling Law for Post-Training After Model Pruning
Xiaodong Chen1,2, Yuxuan Hu1,2, Xiaokang Zhang1,2, Yanling Wang3
Cuiping Li1,2, Hong Chen1,2, Jing Zhang1,2*
1School of Information, Renmin University of China, Beijing, China
2Key Laboratory of Data Engineering and Knowledge Engineering, Beijing, China
3 Zhong... |
synthetic_cpt | 5 | Genie_Achieving_Human_Parity_in_Content-Grounded_Datasets_Generation.pdf | Text Generation with Diffusion Language Models: A Pre-training Approach
with Continuous Paragraph Denoise
Zhenghao Lin 1 2 Yeyun Gong 3 Yelong Shen 4 Tong Wu 5 2 Zhihao Fan 6 2
Chen Lin 1 Nan Duan 3 Weizhu Chen 4
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In this paper, we intro... |
synthetic_cpt | 4 | Differentially_Private_Knowledge_Distillation_via_Synthetic_Text_Generation.pdf | Differentially Private Knowledge Distillation via Synthetic Text Generation
James Flemings Murali Annavaram
University of Southern California
{jamesf17, annavara}@usc.edu
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Abstract
Large Language models (LLMs) are achiev-
ing state-of-the-art per... |
synthetic_cpt | 2 | Emergence_of_In-Context_Reinforcement_Learning_from_Noise_Distillation.pdf | 6
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Emergence is coupled to scope, not level
Alex Ryan
September 2006
Abstract
Since its application to systems, emergence has been explained in terms of levels
of observation. This approach has led to confusion, contradiction, incoherence
a... |
synthetic_cpt | 3 | Meta-Learning_the_Difference_Preparing_Large_Language_Models_for_Efficient_Adaptation.pdf | 3
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JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
1
DAC-MR: Data Augmentation Consistency
Based Meta-Regularization for Meta-Learning
Jun Shu, Xiang Yuan, Deyu Meng, and Zongben Xu
Abstract—Meta learning recently has been heavily resea... |
synthetic_cpt | 4 | Language_Models_can_Self-Lengthen_to_Generate_Long_Texts.pdf | 4
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EFFICACY OF LANGUAGE MODEL SELF-PLAY IN
NON-ZERO-SUM GAMES
Austen Liao∗, Nicholas Tomlin∗, & Dan Klein
Computer Science Division
University of California, Berkeley
{austenliao,nicholas tomlin,klein}@berkeley.edu
ABSTRACT
Game-playing agents like Al... |
synthetic_cpt | 3 | Effective_Large_Language_Model_Adaptation_for_Improved_Grounding_and_Citation_Generation.pdf | Effective Large Language Model Adaptation for Improved
Grounding and Citation Generation
Xi Ye♢∗ Ruoxi Sun♠ Sercan Ö. Arık♠ Tomas Pfister♠
♢ The University of Texas at Austin
♠ Google Cloud AI
♢xiye@cs.utexas.edu
♠{ruoxis,soarik,tpfister}@google.com
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synthetic_cpt | 1 | MAUVE_Measuring_the_Gap_Between_Neural_Text_and_Human_Text_using_Divergence_Frontiers.pdf | 1
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MAUVE: Measuring the Gap
Between Neural Text and Human Text
using Divergence Frontiers
Krishna Pillutla1 Swabha Swayamdipta2 Rowan Zellers1
John Thickstun3
Sean Welleck1,2 Yejin Choi1,2 Zaid Harchaoui4
1Paul G. Allen School of Computer Science ... |
synthetic_cpt | 3 | On_Extracting_Specialized_Code_Abilities_from_Large_Language_Models_A_Feasibility_Study.pdf | 4
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Preprint.
EXTRACTING AND TRANSFERRING ABILITIES
FOR BUILDING MULTI-LINGUAL ABILITY-ENHANCED
LARGE LANGUAGE MODELS
Zhipeng Chen1, Liang Song3, Kun Zhou2, Wayne Xin Zhao1∗, Bingning Wang3∗,
Weipeng Chen3, Ji-Rong Wen1,2
1Gaoling School of Artificia... |
synthetic_cpt | 2 | Language_Models_on_a_Diet_Cost-Efficient_Development_of_Encoders_for_Closely-Related_Languages_via_Additional_Pretraining.pdf | DIET: Lightweight Language Understanding for Dialogue Systems
Tanja Bunk1∗
Daksh Varshneya1†
Vladimir Vlasov1‡
Alan Nichol§
Rasa
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Abstract
Large-scale pre-trained language models have
shown impressive results on language under-
standing bench... |
synthetic_cpt | 1 | AskIt_Unified_Programming_Interface_for_Programming_with_Large_Language_Models.pdf | AskIt: Unified Programming Interface for
Programming with Large Language Models
Katsumi Okuda
CSAIL, MIT
Cambridge, USA
okuda@csail.mit.edu
Mitsubishi Electric Corporation
Amagasaki, Japan
Saman Amarasinghe
CSAIL, MIT
Cambridge, USA
saman@csail.mit.edu
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synthetic_cpt | 9 | Synthetic_continued_pretraining.pdf | 4
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SYNTHETIC CONTINUED PRETRAINING
Zitong Yang∗
Department of Statistics
Stanford University
Neil Band∗
Department of Computer Science
Stanford University
Shuangping Li
Department of Statistics
Stanford University
Emmanuel Cand`es
Department of Sta... |
synthetic_cpt | 1 | Design_of_a_Neuronal_Training_Modeling_Language_Exemplified_with_the_AI-Based_Dynamic_GUI_Adaption.pdf | 3
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Neuron to Graph: Interpreting Language Model
Neurons at Scale
Alex Foote1∗, Neel Nanda2, Esben Kran1, Ioannis Konstas3, Shay B. Cohen4, Fazl Barez1,4,5∗
1Apart Research 2Independent 3 School of Mathematical and Computer Sciences Heriot-Watt Unive... |
synthetic_cpt | 1 | Training_data_augmentation_for_deep_learning_radio_frequency_systems.pdf | Data Augmentation for Deep Learning-based Radio Modulation
Classification
Liang Huang1, Weijian Pan1, You Zhang1, LiPing Qian1, Nan Gao2 and Yuan Wu3
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Abstract— Deep learning has recently been applied to auto-
matically classify the modulation ... |
synthetic_cpt | 3 | Text2Traj2Text_Learning-by-Synthesis_Framework_for_Contextual_Captioning_of_Human_Movement_Trajectories.pdf | Text2Traj2Text: Learning-by-Synthesis Framework for
Contextual Captioning of Human Movement Trajectories
Hikaru Asano1,2* Ryo Yonetani3
1The University of Tokyo
Taiki Sekii3 Hiroki Ouchi4,3,2
2RIKEN AIP
3CyberAgent Inc.
4Nara Institute of Science and Technology
asano-hikaru19@g.ecc.u-tokyo.ac.jp,
{yonetani_ryo, se... |
synthetic_cpt | 3 | Phi-3_Technical_Report_A_Highly_Capable_Language_Model_Locally_on_Your_Phone.pdf | 0
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Approximate wφ
∼
Ωφ Relations in Quintessence Models
Mingxing Luo1∗ and Qi-Ping Su1,2†
1 Zhejiang Institute of Modern Physics, Department of Physics,
Zhejiang University, Hangzhou, Zhejiang 310027, P R China
2 Key Laboratory of Frontiers in... |
synthetic_cpt | 2 | Data_Quality_Control_in_Federated_Instruction-tuning_of_Large_Language_Models.pdf | 4
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Data Quality Control in Federated Instruction-tuning
of Large Language Models
Yaxin Du1
Rui Ye1
Fengting Yuchi1 Wanru Zhao2
Jingjing Qu3
Yanfeng Wang3,1
Siheng Chen1 ∗
1 Shanghai Jiao Tong University
2 University of Cambridge
3 Shanghai A... |
synthetic_cpt | 1 | Can_a_Large_Language_Model_Learn_Matrix_Functions_In_Context.pdf | Can a Large Language Model Learn Matrix
Functions In Context?
Paimon Goulart
Computer Science and Engineering
University of California Riverside
Riverside, CA, USA
paimon.goulart@email.ucr.edu
Evangelos E. Papalexakis
Computer Science and Engineering
University of California Riverside
Riverside, CA, USA
epapalex@cs.u... |
synthetic_cpt | 1 | PAITS_Pretraining_and_Augmentation_for_Irregularly-Sampled_Time_Series.pdf | 9
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A Tuner that
Accelerates Parameters
Felipe M Pait1 and Paulo A Atkinson2
Universidade de S˜ao Paulo
Laborat´orio de Automa¸c˜ao e Controle – pee
S˜ao Paulo SP 05508–900 Brasil
pait,atk@lac.usp.br
Abstract
ential equations (1) and (2), w... |
synthetic_cpt | 1 | Advancing_Enterprise_Spatio-Temporal_Forecasting_Applications_Data_Mining_Meets_Instruction_Tuning_of_Language_Models_For_Multi-modal_Time_Series_Analysis_in_Low-Resource_Settings.pdf | 3
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1
A Comprehensive Survey on Enterprise
Financial Risk Analysis from Big Data
Perspective
Yu Zhao*, Member, IEEE, Huaming Du*, Member, IEEE, Qing Li, Member, IEEE,
Fuzhen Zhuang, Member, IEEE, Ji Liu, Member, IEEE, Gang Kou
Abstract—Enterpri... |
synthetic_cpt | 1 | Holdout-Based_Empirical_Assessment_of_Mixed-Type_Synthetic_Data.pdf | 4
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The Benefits and Risks of Transductive Approaches for AI
Fairness
Muhammed T. Razzak
OATML
University of Oxford
Andreas Kirsch
University of Oxford
Yarin Gal
OATML
University of Oxford
muhammed.razzak@cs.ox.ac.uk
Abstract
Recently, transduc... |
synthetic_cpt | 6 | ALMA_Alignment_with_Minimal_Annotation.pdf | Mem. S.A.It. Vol. 75, 282
c(cid:13) SAIt 2008
Memorie della
The exciting future of (sub-)millimeter
interferometry: ALMA
V. Casasola and J. Brand
INAF – Istituto di Radioastronomia & Italian ALMA Regional Centre
Via P. Gobetti 101, 40129 Bologna, Italy
e-mail: casasola@ira.inaf.it; brand@ira.inaf.it
interferometer... |
synthetic_cpt | 2 | Low-Rank_Adaptation_with_Task-Relevant_Feature_Enhancement_for_Fine-tuning_Language_Models.pdf | 9
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Spin dependent structure function g1 at
low x and low Q2
B. Bade lek a,b J. Kiryluk b and J. Kwieci´nski c
a Department of Physics, Uppsala University, P.O.Box 530, 751 21 Uppsala, Sweden
b Institute of Experimental Physics, Warsaw University, Ho˙za 69... |
synthetic_cpt | 2 | for_Going_Beyond_Nouns_With_Vision_&_Language_Models_Using_Synthetic_Data.pdf | 3
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Going Beyond Nouns With Vision & Language Models Using Synthetic Data
Paola Cascante-Bonilla*†1,2 Khaled Shehada∗2,3
James Seale Smith2,4
Sivan Doveh6,7
Donghyun Kim2,7 Rameswar Panda2,7 G ¨ul Varol5 Aude Oliva2,3
Vicente Ordonez1 Rogerio Feri... |
synthetic_cpt | 2 | On_the_Calibration_of_Large_Language_Models_and_Alignment.pdf | Does Alignment Tuning Really Break LLMs’ Internal Confidence?
Hongseok Oh Wonseok Hwang*
University of Seoul
{cxv0519, wonseok.hwang}@uos.ac.kr
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Abstract
Large Language Models (LLMs) have shown
remarkable progress, but their real-world ap-
plicat... |
synthetic_cpt | 1 | CLR-Bench_Evaluating_Large_Language_Models_in_College-level_Reasoning.pdf | 8
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Novel Prediction Techniques Based
on Clusterwise Linear Regression
Igor Gitman
Machine Learning Department
Carnegie Mellon University
igitman@andrew.cmu.edu
Eric Lei
Machine Learning Department
Carnegie Mellon University
elei@andrew.cmu.edu
Jie... |
synthetic_cpt | 3 | SWITCH_Studying_with_Teacher_for_Knowledge_Distillation_of_Large_Language_Models.pdf | A linear state feedback switching rule for global stabilization of switched nonlinear
systems about a nonequilibrium point
Department of Mathematical Sciences, The University of Texas at Dallas 800 West Campbell Road Richardson, TX 75080
Oleg Makarenkov
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synthetic_cpt | 1 | Distributional_Learning_of_Variational_AutoEncoder_Application_to_Synthetic_Data_Generation.pdf | Learning Autoencoders with Relational Regularization
Hongteng Xu * 1 2 Dixin Luo * 2 Ricardo Henao 2 Svati Shah 2 Lawrence Carin 2
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Abstract
A new algorithmic framework is proposed for
learning autoencoders of data distributions. We
minimize the... |
synthetic_cpt | 1 | Optimizing_Handwritten_Digit_Recognition_with_CNN_and_Data_Augmentation_Strategies.pdf | Handwritten image augmentation
Mahendran N
IIT Tirupati
mahendranNNM@gmail.com
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In this paper, we introduce Handwritten augmentation, a
new data augmentation for handwritten character images.
This method focuses on augmenting handwritte... |
synthetic_cpt | 3 | Prompting_to_Distill_Boosting_Data-Free_Knowledge_Distillation_via_Reinforced_Prompt.pdf | 4
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Continual Distillation Learning: An Empirical Study of Knowledge Distillation
in Prompt-based Continual Learning
Qifan Zhang, Yunhui Guo, Yu Xiang
Department of Computer Science
University of Texas at Dallas
{qifan.zhang,yunhui.guo,yu.xiang}@utdal... |
synthetic_cpt | 5 | Exploring_Mathematical_Extrapolation_of_Large_Language_Models_with_Synthetic_Data.pdf | Exploring Mathematical Extrapolation of
Large Language Models with Synthetic Data
Haolong Li*
Tongji Universiy
furlongli322@gmail.com
Yu Ma
Seed Foundation, ByteDance
mayu.1231@bytedance.com
Yinqi Zhang∗
East China Normal University
zhang.inch@gmail.com
Chen Ye†
ESSC Lab, Tongji Universiy
yechen@tongji.edu.cn
Jie ... |
synthetic_cpt | 1 | Style_Variation_as_a_Vantage_Point_for_Code-Switching.pdf | Style Variation as a Vantage Point for Code-Switching
Khyathi Raghavi Chandu, Alan W Black
Language Technologies Institute
Carnegie Mellon University
kchandu@cs.cmu.edu, awb@cs.cmu.edu
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Code-Switching (CS) is a common phenomenon observed ... |
synthetic_cpt | 2 | NB-MLM_Efficient_Domain_Adaptation_of_Masked_Language_Models_for_Sentiment_Analysis.pdf | Giant efficiency of long-range orbital torque in Co/Nb bilayers
Fufu Liu1, Bokai Liang1, Jie Xu1, Chenglong Jia1,2†, Changjun Jiang1,2*
1 Key Laboratory for Magnetism and Magnetic Materials, Ministry of Education, Lanzhou
University, Lanzhou 730000, China
2 Lanzhou Center for Theoretical Physi... |
synthetic_cpt | 7 | Foundational_Large_Language_Models_for_Materials_Research.pdf | 4
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Foundational Large Language Models for Materials
Research
Vaibhav Mishra1,∗, Somaditya Singh1,∗, Dhruv Ahlawat1,∗, Mohd Zaki2,∗,
Vaibhav Bihani3, Hargun Singh Grover3, Biswajit Mishra4, Santiago Miret5,
Mausam1,3,#, N. M... |
synthetic_cpt | 2 | Comparative_Study_on_Synthetic_and_Natural_Error_Analysis_with_BART_&_MarianMT.pdf | Grammatical vs Spelling error correction: An
investigation into the responsiveness of
Transformer based language models using
BART and MarianMT
Rohit Raju1,2, Peeta Basa Pati*,2, SA Gandheesh2, Gayatri Sanjana Sannala2 & Suriya KS2
1University of Colorado Boulder, CO, US, e-mail: rohit.raju@colorado.edu
2Departme... |
synthetic_cpt | 4 | Diversify_and_Conquer_Diversity-Centric_Data_Selection_with_Iterative_Refinement.pdf | 3
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Diversify & Conquer: Outcome-directed Curriculum
RL via Out-of-Distribution Disagreement
Daesol Cho, Seungjae Lee, and H. Jin Kim
Seoul National University
Automation and Systems Research Institute (ASRI)
Artificial Intelligence Institute of Seou... |
synthetic_cpt | 1 | The_impact_of_altering_emission_data_precision_on_compression_efficiency_and_accuracy_of_simulations_of_the_community_multiscale_air_quality_model.pdf | Informatica 39 (2015) 501–505 501
A Multi-Agent based Approach for Simulating the Impact of Human
Behaviours on Air Pollution
Sabri Ghazi
Laboratoire de gestion électronique du document, Computer Science department, University Badji Mokhtar, PO-Box
12, 23000,Annaba, Algeria
E-mail: sabri.ghazi@univ-annaba.dz
... |
synthetic_cpt | 1 | Density_Ratio_Estimation_via_Infinitesimal_Classification.pdf | Density Ratio Estimation via Infinitesimal Classification
Kristy Choi˚
Chenlin Meng˚
Yang Song
Stefano Ermon
Computer Science Department, Stanford University
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Abstract
Density ratio estimation (DRE) is a funda-
mental machine learning techniq... |
synthetic_cpt | 2 | ReLM_Leveraging_Language_Models_for_Enhanced_Chemical_Reaction_Prediction.pdf | VALIDATING LARGE LANGUAGE MODELS WITH RELM
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Michael Kuchnik 1 Virginia Smith 1 George Amvrosiadis 1
ABSTRACT
Although large language models (LLMs) have been touted for their ability to generate natural-sounding text, there
are growing concerns arou... |
synthetic_cpt | 2 | NLPrompt_Noise-Label_Prompt_Learning_for_Vision-Language_Models.pdf | 4
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NLPrompt: Noise-Label Prompt Learning for Vision-Language Models
Bikang Pan1,† Qun Li1,† Xiaoying Tang2 Wei Huang3 Zhen Fang4 Feng Liu5
Jingya Wang1
Jingyi Yu1 Ye Shi1,*
1ShanghaiTech University, Shanghai, China
2The Chinese University of Hong Ko... |
synthetic_cpt | 3 | TabuLa_Harnessing_Language_Models_for_Tabular_Data_Synthesis.pdf | 7
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Tabula: A Language to Model Spreadsheet Tables
Jorge Mendes and João Saraiva
HASLab, INESC TEC and Universidade do Minho, Portugal
{jorgemendes,saraiva}@di.uminho.pt
Abstract. Spreadsheets provide a flexible and easy to use software de-
velopme... |
synthetic_cpt | 1 | Generation_of_Synthetic_Data_to_Improve_Security_Monitoring_for_Cyber-Physical_Production_Systems.pdf | 0
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Design and Evaluation of A Cyber-Physical Resilient Power System
Testbed
Preprint, compiled November 30, 2020
Abhijeet Sahu1, Patrick Wlazlo2, Zeyu Mao1, Hao Huang1, Ana Goulart2, Katherine Davis1, and Saman Zonouz3... |
synthetic_cpt | 2 | Going_Beyond_Nouns_With_Vision_&_Language_Models_Using_Synthetic_Data.pdf | 3
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Going Beyond Nouns With Vision & Language Models Using Synthetic Data
Paola Cascante-Bonilla*†1,2 Khaled Shehada∗2,3
James Seale Smith2,4
Sivan Doveh6,7
Donghyun Kim2,7 Rameswar Panda2,7 G ¨ul Varol5 Aude Oliva2,3
Vicente Ordonez1 Rogerio Feri... |
synthetic_cpt | 3 | Preference_Fine-Tuning_of_LLMs_Should_Leverage_Suboptimal_On-Policy_Data.pdf | Direct Preference Optimization with an Offset
Afra Amini
Tim Vieira
Ryan Cotterell
{afra.amini, ryan.cotterell}@inf.ethz.ch
tim.f.vieira@gmail.com
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Abstract
Direct preference optimization (DPO) is a
successful fine-tuning strategy for aligning... |
synthetic_cpt | 1 | Auto-CORPus_Automated_and_Consistent_Outputs_from_Research_Publications.pdf | 9
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SIMPLE CURRENT AUTO-EQUIVALENCES OF MODULAR TENSOR
CATEGORIES
CAIN EDIE-MICHELL
Abstract. In this short note we investigate the process of constructing auto-equivalences of
modular tensor categories using invertible objects. We derive condi... |
synthetic_cpt | 8 | Unnatural_Instructions_Tuning_Language_Models_with_(Almost)_No_Human_Labor.pdf | Unnatural Instructions:
Tuning Language Models with (Almost) No Human Labor
Or Honovichτ
Thomas Scialomµ
Omer Levyτ µ
Timo Schickµ
τ Tel Aviv University
µ Meta AI
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Abstract
Instruction tuning enables pretrained language
models to perform new t... |
synthetic_cpt | 7 | Self-calibration_for_Language_Model_Quantization_and_Pruning.pdf | 1
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Non-abelian self-duality from self-interaction
A. Khoudeir
Instituto de F´ısica, Universidad Nacional Aut´onoma de M´exico
Apdo. Postal 20-364, 01000 M´exico D. F. M´exico
and
Centro de Astrof´ısica Te´orica, Departamento de F´ısica, Facultad de
Ciencia... |
synthetic_cpt | 4 | Data_Augmentation_for_Neural_Machine_Translation_using_Generative_Language_Model.pdf | Combining SMT and NMT Back-Translated Data for Efficient NMT
Alberto Poncelas, Maja Popovi´c, Dimitar Shterionov,
Gideon Maillette de Buy Wenniger and Andy Way
School of Computing, DCU, ADAPT Centre
{firstname.lastname}@adaptcentre.ie
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Abstract
Neur... |
synthetic_cpt | 2 | BoostAdapter_Improving_Vision-Language_Test-Time_Adaptation_via_Regional_Bootstrapping.pdf | 4
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BoostAdapter: Improving Vision-Language Test-Time
Adaptation via Regional Bootstrapping
Taolin Zhang1
Jinpeng Wang 1 Hang Guo 1
Tao Dai∗ 2 Bin Chen 3
1 Tsinghua University
3 Harbin Institute of Technology
Shu-tao Xia 1,4
2 Shenzhen University
... |
synthetic_cpt | 2 | Test-Time_Alignment_via_Hypothesis_Reweighting.pdf | 8
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Fault Detection Effectiveness of Source Test Case Generation
Strategies for Metamorphic Testing
Prashanta Saha
School of Computing, Montana State University
Bozeman, Montana
p66n633@msu.montana.edu
Upulee Kanewala∗
School of Computing, Montana St... |
synthetic_cpt | 1 | Grounding_Language_Models_to_Images_for_Multimodal_Generation.pdf | 3
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CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation
Zineng Tang1,4* Ziyi Yang2† Mahmoud Khademi3 Yang Liu2 Chenguang Zhu3‡ Mohit Bansal4†
1UC Berkeley
2Microsoft Azure AI
3Zoom 4UNC Chapel Hill
https://codi-2.github.io
Abs... |
synthetic_cpt | 2 | BoostAdapter_Improving_Training-free_Test-Time_Adaptation_via_Regional_Bootstrapping.pdf | 4
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BoostAdapter: Improving Vision-Language Test-Time
Adaptation via Regional Bootstrapping
Taolin Zhang1
Jinpeng Wang 1 Hang Guo 1
Tao Dai∗ 2 Bin Chen 3
1 Tsinghua University
3 Harbin Institute of Technology
Shu-tao Xia 1,4
2 Shenzhen University
... |
synthetic_cpt | 2 | Prompt_Discriminative_Language_Models_for_Domain_Adaptation.pdf | Domain-Agnostic Mutual Prompting for Unsupervised Domain Adaptation
Zhekai Du1, Xinyao Li1, Fengling Li2, Ke Lu1, Lei Zhu3, Jingjing Li*1
1University of Electronic Science and Technology of China;
2University of Technology Sydney; 3Tongji University
{zhekaid, xinyao326}@std.uestc.edu.cn, lijin117@yeah.net
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synthetic_cpt | 4 | Training_Language_Models_on_Synthetic_Edit_Sequences_Improves_Code_Synthesis.pdf | 4
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TRAINING LANGUAGE MODELS ON SYNTHETIC EDIT
SEQUENCES IMPROVES CODE SYNTHESIS
Ulyana Piterbarg, Lerrel Pinto, Rob Fergus ∗
New York University
up2021@cims.nyu.edu
ABSTRACT
Software engineers mainly write code by editing existing programs. In con... |
synthetic_cpt | 1 | LLM4DS_Evaluating_Large_Language_Models_for_Data_Science_Code_Generation.pdf | LLM4DS: Evaluating Large Language Models for
Data Science Code Generation
Nathalia Nascimento
EASER, Eng. Division
Pennsylvania State University
Great Valley, USA
nqm5742@psu.edu
Everton Guimaraes
EASER, Eng. Division
Pennsylvania State University
Great Valley, USA
ezt157@psu.edu
Sai Sanjna Chintakunta
EASER, Eng. D... |
synthetic_cpt | 2 | P-Flow_A_Fast_and_Data-Efficient_Zero-Shot_TTS_through_Speech_Prompting.pdf | EPJ manuscript No.
(will be inserted by the editor)
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Decay Properties of the Roper Resonance from pp → ppπ+π−
H. Clement representing the PROMICE/WASA collaboration
Physikalisches Institut der Universit¨at T¨ubingen, Morgenstelle 14, D-72076 T¨ubingen
... |
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